Soft missing-feature mask generation for robot audition

PALADYN Journal of Behavioral Robotics

Research Article DOI: 10.2478/s13230-010-0005-1 JBR 1(1) 2010 37-47

Soft missing-feature mask generation for Robot Audition

Toru Takahashi1, Kazuhiro

Nakadai23 , Kazunori Komatani1,

Tetsuya Ogata1, Hiroshi G. Okuno1

1 Department of Intelligence and Science

and Technology

Graduate School of Informatics,

Kyoto University, Kyoto 606-8501, Japan

2 Honda Research Institute Japan Co., Ltd.,

8-1 Honcho, Wako, Saitama 351-0114, Japan,

3 Mechanical and Environmental Informatics,

Graduate School of Information Science and

Engineering, Tokyo Institute of Technology,

Tokyo, 152-8552, Japan

Received 21 February 2010

Accepted 19 March 2010

Abstract

This paper describes an improvement in automatic speech recognition (ASR) for robot audition by introducing Miss-

ing Feature Theory (MFT) based on soft missing feature masks (MFM) to realize natural human-robot interaction. In

an everyday environment, a robots microphones capture various sounds besides the users utterances. Although

sound-source separation is an effective way to enhance the users utterances, it inevitably produces errors due to

reflection and reverberation. MFT is able to cope with these errors. First, MFMs are generated based on the reli-

ability of time-frequency components. Then ASR weighs the time-frequency components according to the MFMs.

We propose a new method to automatically generate soft MFMs, consisting of continuous values from 0 to 1 based

on a sigmoid function. The proposed MFM generation was implemented for HRP-2 using HARK, our open-sourced

robot audition software. Preliminary results show that the soft MFM outperformed a hard (binary) MFM in recogniz-

ing three simultaneous utterances. In a human-robot interaction task, the interval limitations between two adjacent

loudspeakers were reduced from 60 degrees to 30 degrees by using soft MFMs.

Keywords

Robot Audition HARK missing-feature-theory soft mask generation simultaneous speech recognition Automatic Speech Recog-nition sound source separation sound localization

1. Introduction

Human-robot interaction (HRI) is one of the most essential topics in be-

havioral robotics. HRI is improved by the inclusion of a natural speech

communication function with robot-embedded microphones because

we generally use speech in our daily communication. In an everyday

environment a user may barge in or interrupt a robot while it is speak-

ing, or several users may speak at the same time, which is termed si-

multaneous speech. In addition, the robot itself generates sounds due

to its fans and actuators, so the robot must be able to deal with multi-

ple sound sources simultaneously. A conventional approach in human-

robot interaction is to use microphones near the speakers mouth to col-

lect only the desired speech. Kismet of MIT has a pair of microphones

with pinnae, but a human partner still used a microphone close to the

speakers mouth [4]. A group communication robot, Robita of Waseda

University, assumes that each human participant uses a headset micro-

phone [16]. Thus, Robot Audition was proposed to realize the hearing

capability that allows a robot to listen to several things simultaneously

by using the its embedded microphones in [18].

Robot audition has now been actively studied for more than ten years,

as typified by organized sessions on robot audition at the IEEE/RAS

International Conferences on Intelligent Robots and Systems (IROS

20042009), and also a special session on robot audition at the IEEE

International Conference on Acoustics Speech and Signal Processing

E-mail: {tall,komatani,ogata,okuno}@kuis.kyoto-u.ac.jpE-mail: [email protected]

(ICASSP 2009) of the Signal Processing Society. Sound source sepa-

ration as pre-processing of automatic speech recognition (ASR) is an

actively-studied research topic in this field.

Hara et al. reported a humanoid robot, HRP-2, which uses a micro-

phone array to localize and separate a mixture of sounds, and which is

capable of recognizing speech commands in a noisy environment [12].

HRP-2 can recognize one speakers utterance under noisy or interfering

speakers. Nakadai et al. reported SIG, a humanoid robot which uses a

pair of microphones to separate multiple speech signals through an ac-

tive direction-pass filter, and recognizes each separated speech phrase

using ASR [20]. They demonstrated that even when three speakers

utter words at the same time, SIG was able to recognize what each

speaker said. However, since their system used 51 acoustic models

trained under different conditions at the same time, the system incurs

a high computational cost, and performance deteriorates in an environ-

ment with unexpected and/or dynamically changing noises. Kim et al.

have developed another binaural sound-source localization and sep-

aration method by integrating sound-source localization obtained by

CSP (Cross-power Spectrum Phase) and that obtained by visual infor-

mation with an EM algorithm [14]. This system assumes that only one

predominant sound exists in each time frame. Valin et al. have devel-

oped sound-source localization and separation by Geometric Source

Separation, and a multi-channel post-filter with 8 microphones to per-

form speaker tracking [32, 33].

Sound-source separation is an ill-posed problem, however, because

it is impossible to perfectly estimate the effect of reverberation and

environmental noises which change dynamically using microphones

embedded in a mobile robot. Thus, sound-source separation pro-

duces separation errors. To remove such errors, a non-linear speech

enhancement method such as Minima Controlled Recursive Average

37


(MCRA) [5] or Minimum Mean Square Error (MMSE) [10] is often used.

Indeed, non-linear speech enhancement removes the separation er-

rors, but it also generates some distortions like musical noise, which

drops ASR performance.

ASR systems, on the other hand, assume that the input speech is clean

or contaminated with a known noise source, because their target is

mainly telephony applications, which generally involve a high signal-to-

noise ratio (SNR).

There is, therefore, a mismatch between pre-processing sound source

separation and ASR systems. It follows that one of the most important

issues in robot audition is integration between preprocessing andASR.

1.1. Missing Feature Theory

Missing Feature Theory (MFT) is a promising approach for integration

between pre-processing and ASR. MFT is a technique which is know

to improve the noise-robustness of speech recognition by masking out

unreliable acoustic features using a so-called missing feature mask

(MFM) [6, 15, 27]. The effectiveness of MFT has been widely reported

in connected digit recognition for telephony applications [1, 8], speaker

verification [9, 23], de-reverberation [24], and recognition of separated

speech in a binaural way [35].

Yamamoto and Nakadai et al. are the first research group to introduce

a Missing Feature Theory (MFT) to integrate ASR with a binaural robot

audition system [39]. First, the reliability of each time-frequency (TF)

component was estimated by comparing separated speech with the

corresponding clean speech. Then, a hard MFM consisting of 0 or 1

for each TF component was generated based on the reliability using

a manually-defined threshold. Since this mask generation algorithm

used reference speech signals to estimate the reliability, the generated

MFM is called a priori MFM. Although they used a priori hard MFM,they showed a remarkable improvement in the speech recognition of

separated sounds. This showed the effectiveness of MFT approaches.

Automatic MFM generation rises as an issue; actually, this is the pri-mary issue in MFT approaches, and remains an open question despite

numerous MFT studies. Although most works on automatic MFM gen-

eration focus on single-channel input, or on binaural input, Yamamoto

and Valin et al. have developed an automatic MFM generation process

based on microphone-array processing [37].

First, they showed that unreliable features generated by pre-processing

are mainly caused by energy leakage from other sound sources. A

microphone-array-based technique was developed to estimate the re-

liability of each time-frequency component from this energy leakage, by

considering the properties of a multi-channel post-filter process and en-

vironmental noises. Their automatic MFM generation was able to cor-

rectly estimate around 70% of unreliable TF components, compared to

a prioriMFM. Thus, the ASR performance drastically improved, and si-multaneous speech recognition of three voices was attained. However,

they still used a hard binary MFM consisting of a value equal to 0 or 1,

while the reliability of each TF component is estimated as a continuous

value in the range 0 to 1.

This means that some useful information which is contained in the es-

timated reliability may be lost if hard MFM is used.

1.2. Soft Missing Feature Mask

A soft MFM with a continuous value from 0 to 1 was reported as a

better masking approach[27] than hard MFM,both because soft mask-

ing can directly deal with the reliability of an input signal, and because

probabilistic methods can be applied at the same time. Bayesian mask

estimation algorithms were proposed in [28, 29], while Barker et al.[2]

M-SourceSimultaneous

Speeches

MFM

SeparatedSignal

GeometricSource

Separation

Multi-channel

Post-Filter

AcousticFeature

Extraction

Results

MFT-BasedASR

ym sm^

sN

s1

Noise-supressed

Signal

AcousticFeatureAutomatic MFM

Generation

LeakNoise

Estimation

BackgroundNoise

Estimation

bnm

N-channel

SigmoidFunction

R

Figure 1. Geometric source separation with multi-channel post-filter.

used a sigmoid function to estimate a soft MFM. We therefore believe

that a soft MFM also improves the performance of robot audition in

the recognition of pre-processed (separated) speeches. A hard MFM

approach may work when a small number of time-frequency compo-

nents are overlapped between the target speech and a noise, but in

speech-noise cases such as barge-in or simultaneous speech, many

time-frequency components are overlapped. Since a soft masking ap-

proach directly uses reliability, it can also deal with overlapped time-

frequency components properly.

In this paper, we present an automatic, soft-MFM generation method

based on a sigmoid function which is then implemented as a mod-

ule of our open-sourced robot audition software, HARK [21]. To show

the validity of the proposed soft-MFM method, we demonstrate its ef-

fectiveness through tasks including simultaneous speech recognition,

and human-robot interaction involving a humanoid HRP-2 robot taking

a meal order.

The rest of this paper is organized as follows: Section 2 describes the

design of a soft-MFM-generation algorithm for robot audition. Section 3

describes the implementation of a robot-audition system with the pro-

posed soft-MFM generation method, using HARK, our robot audition

software. Section 4 illustrates how HRP-2 receives a meal order by

means of robot audition functions. Section 5 evaluates our proposed

soft-MFM-generation method through recognition of three simultane-

ous speeches and a human-robot interaction scenario. The last section

concludes this paper.

2. The Design of Soft Missing FeatureMask

This section describes the design of our soft MFM which is based on

reliability estimation for time-frequency components. First, the reliability

of the time-frequency component is defined, then separated speeches

are analyzed based on the measured reliability in order to model soft-

MFM generation. Parameter optimization for the modeled soft-MFM

generation is also shown.

2.1. Definition of reliability

Figure 1 shows the core steps of pre-processing in HARK, i.e. Geomet-

ric Source Separation (GSS) [25] and multi-channel post-filtering. GSS

38


is a hybrid sound-source separation method between beam forming

and blind-source separation. Thus, an N-channel input signal whichconsists ofM sound sources sm is separated into each sound source,ym. We use an 8-channel microphone array (N = 8), and the num-ber of sound sources, M, is decided in a sound localization module(see Sec.3.1). As mentioned in the previous section, however, sound-

source separation is an ill-posed problem, and thus ym still includesnon-stationary cross-talk (leakage) and stationary background noises.

Multi-channel post-filtering suppresses both of these types of noise and

produces a noise-suppressed signal sm.The reliability of sm for each time-frequency component (frame and fre-quency indices are omitted for simplification) was defined by

R = sm + bnym . (1)

where bn is a background noise which is separately estimated usingMCRA [5]. Note that R corresponds to leakage level because leakageis a dominant factor in making a time-frequency component unreliable,

as mentioned in the previous section.

2.2. Analysis of separated speech based on reliability

We analyzed the characteristics of R and found that there are twopeaks in the histogram for separated speeches when three speeches

were uttered simultaneously.

One peak corresponds to the leakage components, and the other

matches target-speech components. We checked several intervals

from 10, 20, , 80, 90 degrees, and found the same tendency forevery interval.

2.3. Modeling a soft mask

In hard masking, a hard MFM is generated by thresholding as follows:

HMm ={

1, R > TMFM0, otherwise (2)

where TMFM is a threshold. Dynamic acoustic features, called fea-tures, are commonly used with static acoustic features to improve ASR

performance. features are calculated by linear regression of five con-secutive frames. Let static acoustic features be m(k), features arethen defined by

m(k) = 12i=2 i2

2i=2 i m(k + i), (3)

where k represents frequency indices. Thus, hard masks for featuresare defined in the same way.

HMm(k) =k+2

i=k2,i 6=kHMm(i). (4)

where k now shows the frame index.

Such a linear discrimination with TMFM , however, leads to misclassi-fied time-frequency components; we therefore decided to introduce

soft masking. We assume that these two groups follow Gaussian dis-

tributions. The distribution function for a Gaussian is defined by

d(x) = 12

(1 + erf

( x 2

))(5)

erf(x) = 2 x0 e

t2dt (6)

Let the distribution functions for leakage and target speech be dn(R)and ds(R), respectively. A normalized speech reliability can be definedby

B(R) = ds(R)ds(R) + dn(R) (7)

=1 + erf

(Rss2)

2 + erf(Rss2) erf

(Rnn2) (8)

This is a sigmoid-like function defined using error functions erf(). Sincethere is a high calculation cost for B(R), we decided to use a typicalsigmoid function Q(R) rather than to use this complicated function di-rectly. We then defined a soft MFM based on Q(R) as follows [30]:

SMm = w1Q(R|a, b), (9)Q(x|a, b) =

{ 11+exp(a(xb)) , x > b0, otherwise , (10)

where w1 is an weight factor for static features (0.0 w1). Q(|a, b)is a modified sigmoid function which has two tunable parameters; acorresponds to a trend of the sigmoid function while b represents anx-offset. We also defined soft masks for features as

SMm(k) = w2k+2

i=k2,i6=kQ(R(i|a, b)). (11)

where w2 is an weight factor for dynamic () features (0.0 w2).

2.4. Parameter optimization for soft masking

Figure 2 shows the relationship between soft and hard MFMs. When

a is infinity and w = 1.0 in Equation (10), a soft MFM works as a hardMFM. In this case, b works as threshold, TMFM . Parameters a and bcan be derived from Eqs. (10) and (7), but it is difficult to attain analytical

solutions for them. In addition, for w1 and w2, we have no theoreticalevidence for parameter estimation. We thus measured the recognition

performance of three simultaneous speech signals in order to optimize

these parameters for a robot having eight omni-directional microphones

as shown in Figure 8. Simultaneous speech signals were recorded in a

room with RT20 = 0.35. Three different words were played simultane-ously at the same volume from three loudspeakers located 2m away

39


0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Reliability

Soft

mas

k

a = 140, b = 0.15

a = 80, b = 0.15

a = 20, b = 0.15

Figure 2. Sigmoid function (Eq.(10) for soft mask generation when b = 0.15and a = 20, 80, 140.

Table 1. A search space for soft MFM parameters

parameter range stepa 40 b 0.1 1.5 0.1w1 0.1 1.5 0.1w2 0.1 1.5 0.1

from the robot. Each word was selected from the ATR phonetically bal-

anced wordset consisting of 216 Japanese words. The direction of one

loudspeaker was fixed in front of the robot, and the others were located

at 10, 20, , 80, 90 degrees to the robot. For each configu-ration, 200 combinations of the three different words were played.

Table 1 shows a search space for a soft MFM parameter set p =(a, b, w1, w2). Figure 3 shows an example of w1-w2 parameter op-timization for the center speaker when the loud speakers were located

at 0, 90, and -90 degrees. For other conditions, we obtained a similar

tendency forw1-w2 parameter optimization. We also performed param-eter optimization for a and b, and found that a similar result is obtainedfor every layout. We therefore obtained the optimized parameter set

popt defined by

popt = argmaxp

1

9

90=10

1

3(WC(a, b, w1, w2) +

+WR(a, b, w1, w2) + WL(a, b, w1, w2)) (12)

where WC , WR , and WL indicate the number of correct words foreach of the center, right and left loudspeakers where their locations are

(0, ,) degrees, respectively.Finally, we attained the optimal parameter set for the soft MFM as

popt = (40, 0.5, 0.1, 0.2).

76

76

78

78

78

78

80

80

80 80

80

82

82

82

82

82

82

82

82

84

84

84

84

86

86

8688

delta weight (w2)

sta

tic

wei

gh

t (w

1)

Center speaker in 90deg. condition

0.5 1 1.5

0.5

1

Figure 3. ASR Performance for the center loudspeaker in a word correct rate.This is the case where three loudspeakers were located at (0. 90.-90). This shows the results for the parameters w1 and w2.

3. A Robot Audition System

Our robot audition system consists of five major components shown

in Figure 1. Our proposed soft-MFM generation was described in the

previous section. This section explains the other four components:

A: Geometric Source Separation,

B: Multi-channel post-filter,

C: Acoustic feature extraction,

D: Missing-Feature-Theory-based Automatic Speech Recognition

(MFT-ASR).

Besides the five components, our robot audition system uses several

techniques such as sound-source localization and tracking, which are

described in [38].

3.1. Geometric source separation

GSS is a hybrid algorithm of Blind Source Separation (BSS) and beam-

forming [25]. BSS has a number of limitations such as permutation and

scaling problems, which can be relaxed in GSS by the introduction of

geometric constraints. These are obtained from the locations of mi-

crophones and sound sources. Unlike the Linearly Constrained Mini-

mum Variance (LCMV) beamformer which minimizes the output power

subject to a distortion-less constraint, GSS explicitly minimizes cross-

talk, leading to faster adaptation. The method is also interesting for

use in the mobile robotics context because it allows easy addition and

removal of sources. Using some approximations, it is also possible to

implement separation with relatively low complexity.

Our GSS was modified so as to provide faster adaptation using

stochastic-gradient and shorter time-frame estimation. The locations

of sound sources are estimated with Multiple Signal Classification (MU-

SIC). This is a frequency-domain adaptive beamforming method which

40


produces a sharp local peak corresponding to a sound-source direc-

tion, thus its noise robustness improves in the real world.

To formulate GSS, suppose that there are M sources and N ( M)microphones. A spectrum vector of M sources at frequency , s(),is denoted as [s1()s2() . . . sM ()]

T , and a spectrum vector of sig-

nals captured by the N microphones at frequency , x(), is denotedas [x1()x2() . . . xN ()]

T , where T represents a transpose operator.x() is, then, calculated as

x() = H()s(), (13)

where H() is a transfer function matrix. Each component Hnm of thismatrix represents the transfer function from them-th source to the n-thmicrophone. The source separation is generally formulated as

y() = W ()x(), (14)

whereW () is called a separation matrix. The separation is definedas findingW () which satisfies the condition that output signal y() isthe same as s(). In order to estimateW (), GSS introduces two costfunctions, that is, separation sharpness (JSS ) and geometric constraints(JGC ) defined by

JSS(W ) = E [yyH diag[yyH ]]2, (15)

JGC (W ) = diag[WD I ]2, (16)

where 2 indicates the Frobenius norm, diag[] is the diagonal opera-tor, E [] represents the expectation operator andH represents the con-jugate transpose operator. D shows a transfer function matrix basedon a direct sound path between a sound source and each microphone.

The total cost function J(W ) is represented as

J(W ) = SJSS(W ) + JGC (W ), (17)

where S represents the weighting parameter which controls theweighting between the separation cost and the cost of the geomet-

ric constraint. This parameter is usually set to xHx2 according to[34]. In an online version of GSS,W is updated by minimizing J(W )

W t+1 = W t J(W t), (18)

where W t denotes W at the current time step t, J(W ) is defined as

an update direction ofW , and means a step-size parameter.

3.2. Multi-channel post-filter

A multi-channel post-filter is used to enhance the output of the GSS

algorithm[36]. It is a spectral filter using an optimal noise estimator de-

scribed in [10]. This method is a kind of spectral subtraction [3], but

it generates less musical noises and distortion, because it takes tem-

poral and spectral continuities into account. We extended the original

noise estimator to estimate both stationary and non-stationary noise

by using multi-channel information, while most post-filters only address

the reduction of a specific type of noise: stationary background noise

[17].

The output of GSS y forms an input of the multi-channel post-filter;

An output of the multi-channel post-filter s, is defined as

s = Gy, (19)

where G is a spectral gain. The estimation of G is based on minimummean-square error estimation of spectral amplitude. To estimate G,noise variance is estimated.

The noise variance estimation m is expressed as

m = stat.m +

leakm , (20)

where stat.m is the estimate of the stationary component of the noisefor source m, at frame t for frequency f , and leakm is the estimate ofsource leakage.

We computed the stationary noise estimate, stat.m , using MCRA tech-nique [5]. To estimate leakm , we assumed that the interference fromother sources is reduced by factor (typically -10dB -5 dB) byLSS. The leakage estimate is thus expressed as

leakm = M1

i=0,i 6=mZi, (21)

where Zi is the smoothed spectrum of the m-th source, Ym and recur-sively defined (with 0.7) [40]:

Zm(f, t) = Zm(f, t 1) + (1 )Ym(f, t). (22)

3.3. Acoustic feature extraction

To estimate the reliability of acoustic features, we have to exploit the

fact that noises and distortions are usually concentrated in some ar-

eas in the spectro-temporal space. Most conventional ASR systems

useMel-Frequency Cepstral Coecient (MFCC) [26] as an acous-tic feature, but noises and distortions are spread to all coefficients in

MFCC. In general, Cepstrum-based acoustic features like MFCC are

not suitable for MFT-ASR, Therefore, we use Mel-Scale Log Spec-trum (MSLS) as an acoustic feature.MSLS is obtained by applying inverse discrete cosine transformation

to MFCCs. Three normalization processes are then applied in order

to obtain noise-robust acoustic features: mean-power normalization,

spectrum-peak emphasis and spectrum-mean normalization. The de-

tails are described in [22]. These three normalization processes corre-

spond to three normalization performed against MFCC; C0 normaliza-

tion, liftering, and Cepstrum mean normalization. The acoustic-feature

vector composes 13 MSLS features, their derivatives and log power,i.e., a 27-dimensional MSLS-based acoustic vector was used.

3.4. Missing Feature Theory based ASR

Several robot audition systems with pre-processing and ASR have

been reported so far [11, 19]. Such systems just combine pre-

processing with ASR, and focus on the improvement of SNR and real-

time processing.

Two critical issues remain: what kinds of pre-processing are required

for ASR, and how does ASR use the characteristics of pre-processing

besides using an acoustic model with multi-condition training. We ex-

ploited an interfacing scheme between preprocessing and ASR based

on MFT.

41


MFT uses MFMs in a temporal-frequency map to improve ASR. Each

MFM specifies whether a spectral value for a frequency bin in a specific

time frame is reliable or not. Unreliable acoustic features caused by er-

rors in preprocessing are masked using MFMs, and only reliable ones

are used for a likelihood calculation in the ASR decoder. The decoder

is an HMM-based recognizer, which is commonly used in conventional

ASR systems. The estimation process of output probability in the de-

coder is modified in MFT-ASR.

Let M(i) be a MFM vector that represents the reliability of the i-thacoustic feature. The output probability bj (x) is given by the follow-ing equation:

bj (x) =Ll=1

P(l|Sj ) exp

{Ni=1

M(i) log f(x(i)|l, Sj )

}, (23)

where P() is a probability operator, x(i) is an acoustic feature vector,N is the size of the acoustic feature vector, and Sj is the j-th state.

For implementation, we used Multiband Julian [13], which is based

on the Japanese real-time large-vocabulary speech-recognition engine,

Julian [31]. It supports various HMM types such as shared-state tri-

phones and tied-mixture models. Network grammar is supported for

a language model. It can work as a stand-alone or client-server ap-

plication. To run as a server, we modified the system to be able to

communicate acoustic features and MFM via a network.

4. System Implementation

This section introduces our open-sourced robot audition software,

HARK, then we describe implementation of the proposed soft-MFM

generation as a new module for HARK, and a robot audition system

with the new module.

4.1. Open-Sourced Robot Audition Software HARK

We consider a software environment for robot audition research. Most

studies focus on their own robot platforms, and their systems are un-

available for other researchers and research groups. It is pleasant

and useful for robot audition researchers to share a common platform,

because the researchers do not need to make their own robot plat-

forms from scratch, and they can easily change a module to compare

it with another. Thus, we implemented each technique described in

the previous sections as a component for modular-based architecture

called FlowDesigner [7]1. FlowDesigner provides a flexible and effi-

cient software development environment, which is achieved by flex-

ible replacement of modules and fast data communication between

modules. These advantages are achieved by the pull architecture of

FlowDesigner.

We then released a set of components as HARK (Honda Research

Institute Japan Audition for Robots with Kyoto University; the word

also means of listen in old English).2 [21]. HARK provides a user-

customizable total robot audition system including multi-channel sound

acquisition, sound localization, sound separation and ASR. HARK also

1 http://owdesigner.sourceforge.net/2 It is available at http://winnie.kuis.kyoto-u.ac.jp/HARK/.

provides opportunities to discuss general applicability, platform depen-

dency, customization and tuning. Difficulties such as customization to

another platform or tuning to another acoustic environment have not

yet been discussed. In addition, HARK has a possibility to stimulate

the robot audition research area, and to provide an effective tool for

interdisciplinary research such as natural language processing, naviga-

tion, and HRI. According to user feedback, performance and stability

of HARK will improve.

As related work, Valin released sound-source localization and separa-

tion software for robots called "ManyEars" as General Public License

(GPL) open-source software (OSS). This is the first software which

can provide generally-applicable and customizable robot audition sys-

tems. The only missing function is ASR. "ManyEars" is limited to sound-

source localization and separation. ASR has yet not been included as

it has a lot of parameters which affect the performance of a total robot

audition system.

4.2. Implementation of soft MFM generation forHARK

Figure 4 shows an example of a robot audition system constructed

using HARK. A green rectangle represents a module, while a con-

nection between modules is indicated by a black arrow. The top-

left module (AudioStreamFromMic) captures sounds using a robot-embedded microphone array. After frequency analysis (MultiFFT),sound sources are localized using LocalizeMUSIC,SourceTracker,and SourceIntervalExtender. The localized sound sources are sep-arated with GSS, and PostFilter enhances the separated sounds.MSLS features are calculated using MSLSExtraction, Delta, andFeatureRemover in a Mel-frequency domain (MelFilterBank). TheMFM is estimated in SMFMGeneration. Finally, MSLS features andthe corresponding MFMs are sent to MFT-ASR via a Socket interface

usingSpeechRecognitionClient. Thesemodules are prepared in ad-vance. Thus, users can easily construct their own robot audition sys-

tems by selecting modules and connecting them using a GUI interface.

The newly-developed module is shown as SMFMGeneration at thecenter of Figure 4. It has four terminals which are shown as black dots

on the left and right edges of the box. Three terminals on the left edge

correspond to the input signals such as sm, ym, and bn in Equation (1).The last terminal on the right edge shows the output, that is, a soft-

MFM vector for a frame. This module also has three parameters such

as FBANK, THRESHOLD, and TILT shown in the property setting

window in Figure 5, which appears by double-clicking the green box

of SMFMGeneration. FBANK represents the number of dimensionsfor a static part of a MFM vector defined in Eq. (9), that is 13 in our set-

ting. THRESHOLD and TILT correspond to a and b defined in Eq. (10),respectively.

5. Evaluation

To evaluate the proposed robot audition system with soft-MFM gener-

ation, simultaneous speech recognition was performed in a manner of

isolated word recognition. Also, the systemwas introduced to a human-

robot interaction scenario, that is, a with the robot taking a meal order.

5.1. Experimental setup

We used a humanoid robot HRP-2 with eight microphones around the

top of the head for an experiment of simultaneous speech recognition.

It was placed at the center of a circle in Figure 8. Three loudspeakers

42


Figure 4. A robot audition system constructed by HARK modules.

Figure 5. A parameter tab of a soft mask generation module.

were used to play three speeches simultaneously. A loudspeaker was

fixed in front of the robot, and two other loudspeakers were located

at 30, 60, 90, 120, or 150 shown in Table 2. The distancebetween the robot and each loudspeaker was 1m. Four combinations

of sound sources were used shown in Table 3. Thus, we generated

20 test data sets (3 4). Each test dataset consists of 200 combina-tions of three different words randomly-selected from ATR phonetically

balanced 216 Japanese words.

For an acoustic model in ASR, we trained a 3-state and 16-mixture tri-

phone model based on Hidden Markov Model (HMM) using 27 dimen-

sional MSLS features. To make evaluation fair, we performed an open

test, that is, the acoustic model was trained with a different speech

corpus from test data. For training data, we used a Japanese News

a) A humanoid robot HRP-2.

b) Layout of microphones.

Figure 8. A humanoid robot HRP-2 with an 8 ch microphone array.

Article Speech Database containing 47,308 utterances by 300 speak-

ers. After adding 20dB of white noise to the speech data, the acoustic

model was trained with the white-noise-added training data, which is a

well-known technique for improving the noise-robustness of an acous-

tic model for ASR.

43


Table 2. Loudspeaker locations.

Layout center (deg.) left (deg.) right (deg.)

LL1 0 30 -30

LL2 0 60 -60

LL3 0 90 -900

LL4 0 120 -120

LL5 0 150 -150

Table 3. Sound Source Combination.

combination center left right

SC1 female 1 female 2 female 3

SC2 female 4 female 5 female 6

SC3 male 1 male 2 male 3

SC4 male 4 male 5 male 6

5.2. Recognition of three simultaneous speeches

For comparison, we evaluated three kinds of MFMs as follows:

1. hard MFM : conventional hard MFM defined by Eqs. (2) and (4).

2. soft MFM (unweighted) : soft MFM defined by Eqs. (9) and (11)

with w1 = w2 = 1.

3. soft MFM (proposed) : the proposed soft MFM defined by

Eqs. (9) and (11) using the optimized MFM parameter set popt .

Word correct rates were measured with these MFMs for every test

dataset described above.

Figsures 911 illustrate averaged word correct rates for the center, left

and right speakers, respectively.

For the center speaker, we can say that our proposed soft MFM drasti-

cally improved ASR performance. For the left or right speaker, while the

improvement was less than that for the center speaker, we still find im-

provements to some extent, especially, when the angle between loud-

speakers is narrow. This difference is caused by the layout of the three

speakers. The sound from the center speaker is affected by both theleft and right speakers, while only the center speaker has a large ef-

fect on each of the side speakers. Thus, the number of overlapping

TF components for the center speaker is larger than that of either the

left or the right speaker individually. Also, their overlapping level for the

center speaker is higher than the others. This proves that the proposed

soft MFM is able to cope with the large number of overlapping TF com-

ponents, even in the highly-overlapped cases. The improvement of

the proposed soft MFM reached around 10 points by averaging three-

speaker cases.

When we focus on the difference between the unweighted soft MFM

and the proposed soft MFM, we can find a similar tendency with respect

to the difference between the soft and the hard MFMs; that is, the opti-

mization of weighting factors is more effective when two speakers are

closer together. This means that weighting factors work effectively to

deal with highly overlapped TF components.

30 60 90 120 150 3050

55

60

65

70

75

80

85

90

95

100

Speaker intervals (degrees)

Wo

rd C

orr

ect

Rat

e (%

)

Soft (w/ weight) Soft (w/o weight) Hard

Figure 9. Word correct rate for the center speaker.

30 60 90 120 150 3050

55

60

65

70

75

80

85

90

95

100


Wo

rd C

orr

ect

Rat

e (%

)


Figure 10. Word correct rate for the left speaker.

5.3. Human-robot interaction scenario

We applied a robot audition system including the proposed soft MFM

generation to a human-robot interaction scenario. Figure 12 shows

snapshots of the scenario. In this scenario, the robot receives ameal or-

der, with three customers simultaneously asking the robot for what they

want. The robot localizes and separates their speeches, and the rec-

ognizes the separated speeches. This demonstration was performed

in the same room mentioned in Section 2.4. With the hard MFM we

were previously using, speakers had to keep more than 60 degree in-

tervals from the neighboring speaker for this kind of real situation, while

benchmark tests show that the system maintains performance even

when the interval between speakers is 30 degrees. This is caused

by dynamically-changing noises in a real-world environment. On the

44


30 60 90 120 150 3050

55

60

65

70

75

80

85

90

95

100


Wo

rd C

orr

ect

Rat

e (%

)


Figure 11. Word correct rate for the right speaker.

other hand, the robot with our proposed system maintained ASR per-

formance even in the case of a 30 degree interval for this scenario. This

means that the proposed soft MFM approach was effective in a real-

world environment where dynamically-changing noises exist to some

extent.

6. Conclusion and future work

We have presented an improvement in automatic speech recognition

of robot audition to allow it to realize natural human-robot interaction

which is a topic essential to behavioral robotics. The missing-feature

theory is adopted to integrate microphone-array-based pre-processing

of sound-source localization and separation. For the missing feature

mask, we used a soft missing feature mask taking a continuous value

between 0 and 1, instead of a conventional hard missing feature mask

taking a binary value, 0 or 1. The soft missing feature mask is generated

automatically by estimating the reliability of a time-frequency compo-

nent based on a sigmoid function. The automatic soft mask generation

is incorporated as a set of modules into the HARK open-sourced robot

audition system.

The resulting HARK-based robot audition system with automatic soft

mask generation improves the performance of automatic speech

recognition in the case of three simultaneous speeches, in particular

for narrower intervals of two adjacent speakers up to 30 degrees. The

conventional system worked for speaker-sepearations greater than 60

degrees. Therefore, the soft mask system provides opportunities to de-

ploy a robot audition system in more realistic multi-party interaction. As

a proof of concept, a humanoid HRP-2 demonstrated the role of taking

three meal orders at the same time.

Future work includes detailed analysis and more applications (for ex-

ample, extensive benchmarking to analyse the performance of auto-

matic speech recognition with wide variations of speaker configuration

under various acoustic environments); and application of the HARK-

based system to actual multi-party interactions. Typical scenarios will

include barge-in utterances, utterances of moving talkers, and recog-

nition while the robot is in motion. These applications are expected

Could you take an order?

May I help you?

3. HRP-2 starts to take orders. 4. Three users simultaneously utter to place an order to HRP-2.

Coffee

Coke

Orangejuice

You will drink coke.

5. HRP-2 rephrases the order to the right user. As each user is located, HRP-2 turns its body toward each user when rephrasing.

6. HRP-2 rephrases the order to the center user.

You will drinkorange juice.

7. HRP-2 rephrases the order to the left user.

Thank you for using our service.

You will drink coffee.

8. HRP-2 thanks the users.

1. HRP-2 waits for a user. 2. A user asks HRP-2 to take an order.

Figure 12. Snapshots of a meal order taking task.

to gather more experience in coping with a mixture of sounds, and to

guide new research towards symbiosis of human and robots through

verbal communication and auditory scene analysis.

Acknowledgments

Our research is partially supported by the Grant-in-Aid for Scientific Re-

search and Global COE Program.

45


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Soft missing-feature mask generation for robot audition

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